Citation

Aknda, Md. Rifat and Farid, Fahmid Al and Uddin, Jia and Mansor, Sarina and Kibria, Muhammad Golam (2025) SCCM: An Interpretable Enhanced Transfer Learning Model for Improved Skin Cancer Classification. BioMedInformatics, 5 (3). p. 43. ISSN 2673-7426

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Abstract

Skin cancer is the most common cancer worldwide, for which early detection is crucial to improve survival rates. Visual inspection and biopsies have limitations, including being error-prone, costly, and time-consuming. Although several deep learning models have been developed, they demonstrate significant limitations. An interpretable and improved transfer learning model for binary skin cancer classification is proposed in this research, which uses the last convolutional block of VGG16 as the feature extractor. The methodology focuses on addressing the existing limitations in skin cancer classification, to support dermatologists and potentially saving lives through advanced, reliable, and accessible AI-driven diagnostic tools. Explainable AI is incorporated for the visualization and explanation of classifications. Multiple optimization techniques are applied to avoid overfitting, ensure stable training, and enhance the classification accuracy of dermoscopic images into benign and malignant classes. The proposed model shows 90.91% classification accuracy, which is better than state-of-the-art models and established approaches in skin cancer classification. An interactive desktop application integrating the model is developed, enabling real-time preliminary screening with offline access.

Item Type:	Article
Uncontrolled Keywords:	Dermoscopic image analysis, explainable AI, good health and well-being, skin cancer, transfer learning
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Nurin Syazwani Azmi
Date Deposited:	06 Nov 2025 07:09
Last Modified:	06 Nov 2025 07:09
URII:	http://shdl.mmu.edu.my/id/eprint/14730

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